In the web2.0 decade, the data has increased in volume, variety and velocity. As a result, effective decision making now requires incorporating a wide variety of data sources and processing this data differently to extract patterns and insights. Saama sixthSENSETM solves this problem by combining Big Data approach with traditional analytics to provide unique insights that help make better business decisions.
Saama sixthSENSETM helps you understand the correlation between your business performance and seemingly diverse information sources such as social media, syndicated data, business systems data as well as unstructured data present in your systems, emails and documents. It does so by extracting unseen patterns from this combined data to answer questions that cannot be answered by traditional analytics or data warehouses – see figure below.
Some of the scenarios that clients have used sixthSENSETM for includes:
- Despite investments in dashboards and operational analytics, most marketing organizations are challenged to show how certain consumer sentiment influences their pricing power in the market. The sixthSENSETM solution combines their internal data (such as sales and marketing systems, as well as internal documents (such as pricing models and promotions plan) with external data (such as syndicated data and social media data) and then uses data science technologies such as pattern recognition and learning, visualization and statistical techniques to show how their customer sentiment impacts sales. Armed with such information, client was able to make informed decisions that influence branding, segmentation and pricing and drive increased sales and/or margins
- A client in high technology industry wanted to protect one of their biggest assets and competitive weapons – their employees, especially in a market that is beginning to heat up. The sixthSENSETM solution combines their employee feedback with data from external sources such as Glassdoor and Twitter feeds to provide managers with a 360 degree view of employee satisfaction.
Whether you are looking to analyze the reason behind rise in insurance claims, understand the cause of patient dropouts, drive real-time pricing decisions based on customer demand, reduce product defects or address financial fraud, you need to combine large volumes of internal and external data and combine pattern recognition methods with traditional analytics methods on this big-data to gain insights. Saama sixthSENSETM can help you in such scenarios with our unique Data Science driven approach. The solution is designed to provide rich insights while hiding the complexity of sophisticated technology underneath its hood from the user. It enables you to easily get a very rich custom analytics solution to address your specific scenario within 8 to 12 weeks.
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